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        data-chapter-title="DataFrame查询2-专用查询"
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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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        <li class="chapter " data-level="4.4.4" data-path="数据分析库的操作/8Pandas分组聚合1.html">
            
                
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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        <li class="chapter " data-level="4.4.8" data-path="数据分析库的操作/16Pandas数据规整-合并.html">
            
                
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
                    </a>
            
            
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            </ul>
            
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        <li class="chapter " data-level="4.5" data-path="数据分析库的操作/13Pandas数据规整-重塑和轴向旋转.html">
            
                
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
                    </a>
            
            
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        <li class="chapter " data-level="4.5.1" data-path="数据分析库的操作/13.1透视表和交叉表.html">
            
                
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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        <li class="chapter " data-level="5" data-path="Python可视化/绘图库-Matplotlib.html">
            
                
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
                    </a>
            
            
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        <li class="chapter " data-level="5.4" data-path="Python可视化/绘图库-Matplotlib/3Matplotlib-高级绘图.html">
            
                
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
                    </a>
            
            
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        <li class="chapter " data-level="5.5" data-path="Python可视化/绘图库-Matplotlib/4数学计算展示图像.html">
            
                
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
                    </a>
            
            
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        <li class="chapter " data-level="5.6" data-path="Python可视化/绘图库-Matplotlib/5注意事项.html">
            
                
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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        <li class="chapter " data-level="5.7" data-path="Python可视化/绘图库-Matplotlib/6pylab.html">
            
                
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                            <b>5.7.</b>
                        
                        拓展：pylab
                    </a>
            
            
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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                        <h2 id="dataframe&#x67E5;&#x8BE2;2&#x4E13;&#x7528;&#x67E5;&#x8BE2;">DataFrame&#x67E5;&#x8BE2;2-&#x4E13;&#x7528;&#x67E5;&#x8BE2;</h2>
<hr>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
</code></pre>
<pre><code class="lang-python">a = pd.DataFrame([
    [<span class="hljs-string">&apos;&#x5C0F;&#x660E;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">18</span>,<span class="hljs-number">170</span>,<span class="hljs-number">60</span>,<span class="hljs-string">&apos;&#x5317;&#x4EAC;&#x6D77;&#x6DC0;&apos;</span>,<span class="hljs-number">61</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x534E;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">28</span>,<span class="hljs-number">160</span>,<span class="hljs-number">50</span>,<span class="hljs-string">&apos;&#x4E0A;&#x6D77;&#x9759;&#x5B89;&apos;</span>,<span class="hljs-number">74</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x7EA2;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">22</span>,<span class="hljs-number">175</span>,<span class="hljs-number">64</span>,<span class="hljs-string">&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;</span>,<span class="hljs-number">59</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x9751;&apos;</span>,<span class="hljs-string">&apos;male&apos;</span>,<span class="hljs-number">31</span>,<span class="hljs-number">182</span>,<span class="hljs-number">80</span>,<span class="hljs-string">&apos;&#x6DF1;&#x5733;&#x5357;&#x5C71;&apos;</span>,<span class="hljs-number">82</span>],
    [<span class="hljs-string">&apos;&#x5C0F;&#x5170;&apos;</span>,<span class="hljs-string">&apos;female&apos;</span>,<span class="hljs-number">25</span>,<span class="hljs-number">165</span>,<span class="hljs-number">55</span>,<span class="hljs-string">&apos;&#x676D;&#x5DDE;&#x897F;&#x6E56;&apos;</span>,<span class="hljs-number">98</span>],
],
     index=[<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>,<span class="hljs-number">5</span>],
    columns=[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;sex&apos;</span>,<span class="hljs-string">&apos;age&apos;</span>,<span class="hljs-string">&apos;heigh&apos;</span>,<span class="hljs-string">&apos;weight&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>,<span class="hljs-string">&apos;grade&apos;</span>]
)
a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<h2 id="pandas&#x4E13;&#x7528;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;">Pandas&#x4E13;&#x7528;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;</h2>
<p>&#x7ECF;&#x8FC7;&#x4F18;&#x5316;&#xFF0C;&#x63A8;&#x8350;</p>
<p>&#x4E09;&#x79CD;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;&#xFF1A;</p>
<p>&#x7D22;&#x5F15;</p>
<p>&#x5207;&#x7247;</p>
<p>&#x8FC7;&#x6EE4;</p>
<hr>
<h2 id="&#x7D22;&#x5F15;&#x67E5;&#x8BE2;">&#x7D22;&#x5F15;&#x67E5;&#x8BE2;</h2>
<pre><code>&#x7528;&#x4E8E;&#x8FDE;&#x7EED;&#x6216;&#x4E0D;&#x8FDE;&#x7EED;(&#x884C;&#x5217;&#x6709;&#x95F4;&#x9694;)&#x884C;&#x5217;&#x533A;&#x5757;&#x67E5;&#x8BE2;

&#x89E3;&#x51B3;&#x4E86;DataFrame&#x8FDB;&#x884C;&#x884C;&#x6807;&#x7B7E;&#x67E5;&#x8BE2;&#x7684;&#x95EE;&#x9898;
</code></pre><h4 id="&#x4E24;&#x79CD;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;&#xFF1A;">&#x4E24;&#x79CD;&#x67E5;&#x8BE2;&#x65B9;&#x5F0F;&#xFF1A;</h4>
<pre><code>a.loc[&#x884C;,&#x5217;]&#xFF0C;&#x6807;&#x7B7E;&#x7D22;&#x5F15;&#xFF0C;&#x81EA;&#x5B9A;&#x4E49;&#x7D22;&#x5F15;

a.iloc[&#x884C;,&#x5217;]&#xFF0C;&#x4F4D;&#x7F6E;&#x7D22;&#x5F15;&#xFF0C;&#x9ED8;&#x8BA4;&#x7D22;&#x5F15;
</code></pre><p>&#x53C2;&#x6570;&#x4E66;&#x5199;&#x987A;&#x5E8F;&#x90FD;&#x662F;&#x90FD;&#x662F;&#x5148;&#x884C;&#x540E;&#x5217;</p>
<pre><code class="lang-python">a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x5355;&#x884C;</p>
<pre><code class="lang-python"><span class="hljs-comment">#&#x81EA;&#x5B9A;&#x4E49;&#x7D22;&#x5F15;</span>
a.loc[<span class="hljs-number">1</span>,:]    <span class="hljs-comment">#&#x81EA;&#x5B9A;&#x4E49;&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x5355;&#x884C;</span>
a.loc[<span class="hljs-number">1</span>]   <span class="hljs-comment">#&#x7701;&#x7565;&#x5217;&#xFF0C;&#x7B80;&#x4FBF;&#x5199;&#x6CD5;</span>

<span class="hljs-comment">#&#x9ED8;&#x8BA4;&#x7D22;&#x5F15;</span>
a.iloc[<span class="hljs-number">0</span>]
</code></pre>
<pre><code>name         &#x5C0F;&#x660E;
sex        male
age          18
heigh       170
weight       60
address    &#x5317;&#x4EAC;&#x6D77;&#x6DC0;
grade        61
Name: 1, dtype: object
</code></pre><pre><code class="lang-python"><span class="hljs-comment"># Series &#x7C7B;&#x578B;</span>
type(a.loc[<span class="hljs-number">1</span>])
</code></pre>
<pre><code>pandas.core.series.Series
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x884C;</p>
<pre><code class="lang-python">a.loc[[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>],:]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">a.iloc[[<span class="hljs-number">0</span>,<span class="hljs-number">2</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
  </tbody>
</table>
</div>




<p>&#x67E5;&#x8BE2;&#x5355;&#x5217;</p>
<pre><code class="lang-python">a.loc[:,<span class="hljs-string">&apos;name&apos;</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
2    &#x5C0F;&#x534E;
3    &#x5C0F;&#x7EA2;
4    &#x5C0F;&#x9751;
5    &#x5C0F;&#x5170;
Name: name, dtype: object
</code></pre><pre><code class="lang-python">a.iloc[:,<span class="hljs-number">0</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
2    &#x5C0F;&#x534E;
3    &#x5C0F;&#x7EA2;
4    &#x5C0F;&#x9751;
5    &#x5C0F;&#x5170;
Name: name, dtype: object
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x5217;</p>
<pre><code class="lang-python">a.loc[:,[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">a.iloc[:, [<span class="hljs-number">0</span>,<span class="hljs-number">5</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x5355;&#x5143;&#x683C;&#xFF0C;&#x67D0;&#x4E00;&#x4E2A;&#x5177;&#x4F53;&#x7684;&#x503C;  &#x5355;&#x884C;&#x5355;&#x5217;&#x4EA4;&#x53C9;</p>
<pre><code class="lang-python">a.loc[<span class="hljs-number">3</span>,<span class="hljs-string">&apos;address&apos;</span>]   <span class="hljs-comment">#&#x884C;&#x5217;&#x4EA4;&#x53C9;&#x5904;</span>
</code></pre>
<pre><code>&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;
</code></pre><pre><code class="lang-python">a.iloc[<span class="hljs-number">2</span>,<span class="hljs-number">5</span>]
</code></pre>
<pre><code>&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;
</code></pre><pre><code class="lang-python"><span class="hljs-comment">#&#x9488;&#x5BF9;&#x5355;&#x5143;&#x683C;&#x7684;&#x7279;&#x6B8A;&#x5199;&#x6CD5;</span>
a.at[<span class="hljs-number">3</span>,<span class="hljs-string">&apos;address&apos;</span>]
</code></pre>
<pre><code>&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;
</code></pre><pre><code class="lang-python">a.iat[<span class="hljs-number">2</span>,<span class="hljs-number">5</span>]
</code></pre>
<pre><code>&apos;&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;&apos;
</code></pre><p>&#x67E5;&#x8BE2;&#x4E00;&#x884C;&#x591A;&#x5217;</p>
<pre><code class="lang-python">a.loc[<span class="hljs-number">3</span>,[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>]]
</code></pre>
<pre><code>name         &#x5C0F;&#x7EA2;
address    &#x5E7F;&#x5DDE;&#x5929;&#x6CB3;
Name: 3, dtype: object
</code></pre><pre><code class="lang-python">a.iloc[<span class="hljs-number">2</span>,[<span class="hljs-number">0</span>,<span class="hljs-number">5</span>]]
</code></pre>
<pre><code>name         &#x5C0F;&#x7EA2;
address    &#x5E7F;&#x5DDE;&#x5929;&#x6CB3;
Name: 3, dtype: object
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x884C;&#x4E00;&#x5217;</p>
<pre><code class="lang-python">a.loc[[<span class="hljs-number">2</span>,<span class="hljs-number">4</span>],<span class="hljs-string">&apos;name&apos;</span>]
</code></pre>
<pre><code>2    &#x5C0F;&#x534E;
4    &#x5C0F;&#x9751;
Name: name, dtype: object
</code></pre><pre><code class="lang-python">a.iloc[[<span class="hljs-number">1</span>,<span class="hljs-number">3</span>],<span class="hljs-number">0</span>]
</code></pre>
<pre><code>2    &#x5C0F;&#x534E;
4    &#x5C0F;&#x9751;
Name: name, dtype: object
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x884C;&#x591A;&#x5217;</p>
<pre><code class="lang-python">a.loc[[<span class="hljs-number">2</span>,<span class="hljs-number">4</span>],[<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;address&apos;</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">a.iloc[[<span class="hljs-number">1</span>,<span class="hljs-number">3</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">5</span>]]
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
  </tbody>
</table>
</div>



<hr>
<h3 id="&#x5207;&#x7247;&#x67E5;&#x8BE2;">&#x5207;&#x7247;&#x67E5;&#x8BE2;</h3>
<pre><code class="lang-python">a
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
      <th>grade</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>male</td>
      <td>18</td>
      <td>170</td>
      <td>60</td>
      <td>&#x5317;&#x4EAC;&#x6D77;&#x6DC0;</td>
      <td>61</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>female</td>
      <td>28</td>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
      <td>74</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>female</td>
      <td>22</td>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
      <td>59</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>male</td>
      <td>31</td>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
      <td>82</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>female</td>
      <td>25</td>
      <td>165</td>
      <td>55</td>
      <td>&#x676D;&#x5DDE;&#x897F;&#x6E56;</td>
      <td>98</td>
    </tr>
  </tbody>
</table>
</div>



<p>&#x67E5;&#x8BE2;&#x5355;&#x884C;&#xFF0C;&#x5355;&#x5217;</p>
<pre><code class="lang-python">a.loc[<span class="hljs-number">1</span>,:]   <span class="hljs-comment"># &#x5355;&#x884C; &#x540C; a.loc[1]</span>
</code></pre>
<pre><code>name         &#x5C0F;&#x660E;
sex        male
age          18
heigh       170
weight       60
address    &#x5317;&#x4EAC;&#x6D77;&#x6DC0;
grade        61
Name: 1, dtype: object
</code></pre><pre><code class="lang-python">a.iloc[<span class="hljs-number">0</span>,:]
</code></pre>
<pre><code>name         &#x5C0F;&#x660E;
sex        male
age          18
heigh       170
weight       60
address    &#x5317;&#x4EAC;&#x6D77;&#x6DC0;
grade        61
Name: 1, dtype: object
</code></pre><pre><code class="lang-python">a.loc[:,<span class="hljs-string">&apos;name&apos;</span>]  <span class="hljs-comment"># &#x5355;&#x5217;</span>
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
2    &#x5C0F;&#x534E;
3    &#x5C0F;&#x7EA2;
4    &#x5C0F;&#x9751;
5    &#x5C0F;&#x5170;
Name: name, dtype: object
</code></pre><pre><code class="lang-python">a.iloc[:,<span class="hljs-number">0</span>]
</code></pre>
<pre><code>1    &#x5C0F;&#x660E;
2    &#x5C0F;&#x534E;
3    &#x5C0F;&#x7EA2;
4    &#x5C0F;&#x9751;
5    &#x5C0F;&#x5170;
Name: name, dtype: object
</code></pre><p>&#x67E5;&#x8BE2;&#x591A;&#x884C;&#x591A;&#x5217;</p>
<pre><code class="lang-python">a.loc[<span class="hljs-number">2</span>:<span class="hljs-number">4</span>,<span class="hljs-string">&apos;heigh&apos;</span>:<span class="hljs-string">&apos;address&apos;</span>]  <span class="hljs-comment">#&#x81EA;&#x5B9A;&#x4E49;&#x7D22;&#x5F15;&#x5207;&#x7247;&#xFF0C;&#x5305;&#x542B;&#x7ED3;&#x675F;&#x503C;</span>
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>3</th>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">a.iloc[<span class="hljs-number">1</span>:<span class="hljs-number">4</span>,<span class="hljs-number">3</span>:<span class="hljs-number">6</span>] <span class="hljs-comment">#&#x9ED8;&#x8BA4;&#x7D22;&#x5F15;&#x5207;&#x7247;&#xFF0C;&#x4E0D;&#x5305;&#x542B;&#x7ED3;&#x675F;&#x503C;</span>
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>heigh</th>
      <th>weight</th>
      <th>address</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>160</td>
      <td>50</td>
      <td>&#x4E0A;&#x6D77;&#x9759;&#x5B89;</td>
    </tr>
    <tr>
      <th>3</th>
      <td>175</td>
      <td>64</td>
      <td>&#x5E7F;&#x5DDE;&#x5929;&#x6CB3;</td>
    </tr>
    <tr>
      <th>4</th>
      <td>182</td>
      <td>80</td>
      <td>&#x6DF1;&#x5733;&#x5357;&#x5C71;</td>
    </tr>
  </tbody>
</table>
</div>



<h4 id="&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x548C;&#x5207;&#x7247;&#x67E5;&#x8BE2;&#x7684;&#x533A;&#x522B;">&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x548C;&#x5207;&#x7247;&#x67E5;&#x8BE2;&#x7684;&#x533A;&#x522B;</h4>
<ul>
<li>&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x66F4;&#x9002;&#x5408;&#x67E5;&#x8BE2;&#x4E0D;&#x8FDE;&#x7EED;&#x7684;&#x6570;&#x636E;</li>
<li>&#x5207;&#x7247;&#x67E5;&#x8BE2;&#x9002;&#x5408;&#x67E5;&#x8BE2;&#x8FDE;&#x7EED;&#x6570;&#x636E;</li>
</ul>
<p>&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x53EF;&#x4EE5;&#x5B9E;&#x73B0;&#x5207;&#x7247;&#x67E5;&#x8BE2;&#x7684;&#x6240;&#x6709;&#x529F;&#x80FD;&#xFF0C;&#x53EA;&#x662F;&#x6709;&#x4E00;&#x4E2A;&#x4E66;&#x5199;&#x6548;&#x7387;&#x95EE;&#x9898;</p>
<ul>
<li>&#x7528;&#x7D22;&#x5F15;&#x67E5;&#x8BE2;&#x67E5;&#x8FDE;&#x7EED;&#x6570;&#x636E;&#xFF0C;&#x9700;&#x8981;&#x5C06;&#x6BCF;&#x4E2A;&#x7D22;&#x5F15;&#x90FD;&#x5199;&#x4E0A;&#xFF0C;&#x6548;&#x7387;&#x4F4E;&#x3002;</li>
<li>&#x5207;&#x7247;&#x67E5;&#x8BE2;&#x67E5;&#x8FDE;&#x7EED;&#x6570;&#x636E;&#xFF0C;&#x53EA;&#x8981;&#x5199;&#x4E0A;&#x8D77;&#x59CB;&#x548C;&#x7ED3;&#x675F;&#x7D22;&#x5F15;&#x5373;&#x53EF;&#x3002;<ul>
<li>&#x5207;&#x7247;&#x4E0D;&#x80FD;&#x67E5;&#x8BE2;&#x4E0D;&#x8FDE;&#x7EED;&#x6570;&#x636E;</li>
</ul>
</li>
</ul>
<p>&#x4F18;&#x5148;&#x4F7F;&#x7528;&#x5207;&#x7247;&#x67E5;&#x8BE2;</p>

                    
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